Getting Started

Installation

The MindLab is the ultimate toolbox for high quality, efficient data science work. You can simply install it from pypi:

pip install mindlab

Plotting

Drawing professional plots is now trivial:

from mindlab import Figure, mock_data

figure = Figure(title='Mock Stock Prices', xtics='month', ylabel='Stock Price [USD]')
figure.line(mock_data.stock_prices())

The mindlab.Figure class provides a convenient but powerful interface to Matplotlib figures. No more fiddling with legends and tick locators, and no more plots with tiny fonts or unreadable tick labels! Make sure to check out the Plotting section of the documentation for a complete reference.

Jupyter

You can install MindLab with Jupyter support too:

pip install mindlab[jupyter]

This provides the lab command which starts a pre-configured JupyterLab session with sensible defaults and auto-completion:

$ lab
...
[I ... ServerApp] Jupyter Server is running at:
[I ... ServerApp] http://localhost:8888/lab?token={token}

Executing queries against various data sources is extremely simple using the provided MindLab Magics (after authentication):

%%bigquery
SELECT title, `by` AS author, DATETIME(`timestamp`) AS posted_at, score
FROM bigquery-public-data.hacker_news.full
WHERE type = 'story'
ORDER BY timestamp NULLS LAST
LIMIT 3

	title	author	posted_at	score
0	Y Combinator	pg	2006-10-09 18:21:51	57
1	A Student's Guide to Startups	phyllis	2006-10-09 18:30:28	16
2	Woz Interview: the early days of Apple	phyllis	2006-10-09 18:40:33	7

Of course, true power lies in combining the magics with MindLab’s plotting capabilities:

%%bigquery scores
SELECT score, COUNT(*) AS frequency, EXTRACT(YEAR FROM `timestamp`) as `year`
FROM bigquery-public-data.hacker_news.full
WHERE type = 'story' AND score IS NOT NULL
      AND `timestamp` >= TIMESTAMP '2007-01-01' AND `timestamp` < TIMESTAMP '2015-01-01'
GROUP BY score, `year`

figure = Figure(
    title='Hacker News Score Distribution',
    xlabel='Score', ylabel='Frequency', xscale='log', yscale='log',
)
figure.scatter(scores.groupby('year'), cmap='viridis', alpha=0.5)